Background Per- and poly-fluoroalkyl substances (PFAS) can influence gestational diabetes mellitus (GDM); however, current studies on their association are limited and have yielded inconsistent findings. Objective To investigate the association between maternal exposure to PFAS, as measured by urinary concentrations in early pregnancy, and the risk of developing GDM. Methods Based on the Wuhu Birth Cohort in Anhui Province conducted between 2020 and 2023, this study included 1910 pregnant women. Urine samples were collected during the first trimester, and a 75 g oral glucose tolerance test (OGTT) was completed at 24–28 gestational weeks. The concentrations of six PFAS in urine were measured using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Participants were categorized into low, medium, and high exposure groups according to tertiles of individual PFAS concentrations. Logistic regression analysis was employed to assess the potential impact of early-pregnancy PFAS exposure levels on the risk of GDM, with stratified analyses by maternal age and fetal sex. A generalized weighted quantile sum regression (gWQS) model was applied to evaluate the relative weight distribution of the six PFAS in their mixture effect. Results The logistic regression results revealed that both medium exposure to perfluorohexane sulfonate (PFHxS) (OR=1.475, 95%CI: 1.040, 2.093) and high exposure to perfluorononanoic acid (PFNA) (OR=1.430, 95%CI: 1.013, 2.018) were positively associated with diagnosed GDM. This association was more pronounced among women older than 28 years. Among pregnant women carrying male fetuses, high-level exposures to per fluorohexanoic acid (PFHxA) (OR=1.708, 95%CI: 1.006, 2.899) and PFHxS (OR=2.116, 95%CI: 1.247, 3.584) showed positive associations with diagnosed GDM. In contrast, among those carrying female fetuses, moderate perfluorooctanesulfonic acid (PFOS) exposure was associated with a lower risk of GDM (OR=0.542, 95%CI: 0.311, 0.946). The gWQS results demonstrated that PFOS (0.48) and perfluorobutane sulfonate (PFBS) (0.34) contributed the most to the mixture effect. Conclusion Early-pregnancy exposure to PFAS (particularly PFHxS and PFNA) may be an etiological factor for GDM, with maternal age and fetal sex potentially acting as effect modifiers in this association.

With the integration of 5G communication technology and robotic surgical systems, remote robot-assisted thoracic surgery is overcoming geographical barriers, offering an innovative approach to addressing the uneven distribution of medical resources. This study conducted a systematic literature review—using databases such as PubMed and CNKI, with the search period extending up to 2025—incorporating clinical studies, case reports, and review articles to comprehensively evaluate the clinical efficacy and safety of 5G-enabled remote robot-assisted thoracic surgery (5G-RRATS). The analysis also examined current technological limitations and potential future development trajectories. Existing evidence indicates that, given adequate technical support, 5G-RRATS can achieve perioperative outcomes comparable to those of conventional local robotic surgeries across procedures including pulmonary wedge resection, lobectomy, and esophagectomy. Furthermore, it demonstrates potential advantages in minimizing surgical incisions and reducing intraoperative blood loss. Nevertheless, challenges related to network stability, latency control, interdisciplinary collaboration between medical and engineering teams, and legal, regulatory, and ethical considerations continue to hinder widespread clinical adoption. Looking ahead, the emergence of a "one-to-many" remote surgical model, combined with the integration of artificial intelligence and augmented reality technologies, as well as advancements in low-orbit satellite communications, may enable 5G-RRATS to further advance precision and efficiency in thoracic surgery, thereby facilitating equitable access to high-quality care for a broader patient population.

ObjectiveTo construct an actor-network for integrating physical activity into rehabilitation services based on the World Health Organization Rehabilitation in Health Service System framework and actor-network theory (ANT). MethodsContent analysis was employed using the six building blocks of health service systems as the theoretical framework. Actors related to rehabilitation services were extracted and categorized into a rehabilitation actor pool, while a physical activity actor pool was formed based on four major physical activity scenarios. Actors from both pools were integrated, deduplicated and classified to form a final list of integrated actors. Using ANT, the construction process of the actor network integrating physical activity into rehabilitation was analyzed through the four stages of translation: problematization, interessment, enrollment and mobilization. ResultsA dynamic integration network was constructed, comprising human actors (patients, rehabilitation professionals, researchers, sports coaches, government departments, medical institutions, community organizations and industry media, etc.) and non-human actors (assistive devices, sports infrastructure, smart equipment, information systems, online exercise guidance systems, laws and regulations, strategic documents, and exercise prescriptions, etc.). The study identified maximizing rehabilitation outcomes as the mandatory passage point and elaborated on the critical role of government departments as focal actors in coordinating various stakeholders. ConclusionThe integration of physical activity into rehabilitation services is a dynamic network constructed by diverse actors through a process of translation. ANT provides an operational theoretical framework for cross-departmental governance of rehabilitation policies in China, promotes the spatial expansion of the rehabilitation field, and drives its transformation toward a networked and ecological system. The government needs to play a leading role in facilitating role reconstruction and synergy among heterogeneous actors in both the sports and rehabilitation sectors through mechanism design, to create a bidirectional empowerment mechanism that fosters mutual progress and ensures the sustainable development of integrated services.

Lung transplantation is a definitive treatment for end-stage lung disease and can significantly improve patient prognosis. However, postoperative complications such as infection, rejection, ischemia-reperfusion injury and chronic lung allograft dysfunction intertwine to form a complex pathological network, posing persistent challenges to long-term patient survival. In 2025, research teams worldwide have made systematic progress in the field of basic lung transplantation research. By integrating cutting-edge technologies including single-cell multi-omics, spatial transcriptomics and novel animal models, significant breakthroughs have been achieved in the evolutionary dynamics of drug-resistant infections, molecular mechanisms of immune regulation, programmed cell death, optimization of donor lung protection strategies and early warning of chronic lung allograft dysfunction. This article systematically reviews the representative advances in basic lung transplantation research worldwide in 2025, and deeply analyzes the implications of mechanistic breakthroughs for optimizing diagnosis and treatment strategies, aiming to anchor the direction for innovative breakthroughs and clinical translation in basic lung transplantation research.

Large language models (LLMs) such as ChatGPT, Claude, Llama, and Qwen are emerging as transformative technologies for the diagnosis and treatment of various diseases. With their exceptional long-context reasoning capabilities, LLMs are proficient in clinically relevant tasks, particularly in medical text analysis and interactive dialogue. They can enhance diagnostic accuracy by processing vast amounts of patient data and medical literature and have demonstrated their utility in diagnosing common diseases and facilitating the identification of rare diseases by recognizing subtle patterns in symptoms and test results. Building on their image-recognition abilities, multimodal LLMs (MLLMs) show promising potential for diagnosis based on radiography, chest computed tomography (CT), electrocardiography (ECG), and common pathological images. These models can also assist in treatment planning by suggesting evidence-based interventions and improving clinical decision support systems through integrated analysis of patient records. Despite these promising developments, significant challenges persist regarding the use of LLMs in medicine, including concerns regarding algorithmic bias, the potential for hallucinations, and the need for rigorous clinical validation. Ethical considerations also underscore the importance of maintaining the function of supervision in clinical practice. This paper highlights the rapid advancements in research on the diagnostic and therapeutic applications of LLMs across different medical disciplines and emphasizes the importance of policymaking, ethical supervision, and multidisciplinary collaboration in promoting more effective and safer clinical applications of LLMs. Future directions include the integration of proprietary clinical knowledge, the investigation of open-source and customized models, and the evaluation of real-time effects in clinical diagnosis and treatment practices.
Humans ; Large Language Models ; Tomography, X-Ray Computed

Objective To compare the performance and efficacy of prognostic models constructed by different machine learning algorithms in predicting the survival period of lung transplantation (LTx) recipients. Methods Data from 483 recipients who underwent LTx were retrospectively collected. All recipients were divided into a training set and a validation set at a ratio of 7:3. The 24 collected variables were screened based on variable importance (VIMP). Prognostic models were constructed using random survival forest (RSF) and extreme gradient boosting tree (XGBoost). The performance of the models was evaluated using the integrated area under the curve (iAUC) and time-dependent area under the curve (tAUC). Results There were no significant statistical differences in the variables between the training set and the validation set. The top 15 variables ranked by VIMP were used for modeling and the length of stay in the intensive care unit (ICU) was determined as the most important factor. Compared with the XGBoost model, the RSF model demonstrated better performance in predicting the survival period of recipients (iAUC 0.773 vs. 0.723). The RSF model also showed better performance in predicting the 6-month survival period (tAUC _{6 months} 0.884 vs. 0.809, P = 0.009) and 1-year survival period (tAUC _{1 year} 0.896 vs. 0.825, P = 0.013) of recipients. Based on the prediction cut-off values of the two algorithms, LTx recipients were divided into high-risk and low-risk groups. The survival analysis results of both models showed that the survival rate of recipients in the high-risk group was significantly lower than that in the low-risk group (P<0.001). Conclusions Compared with XGBoost, the machine learning prognostic model developed based on the RSF algorithm may preferably predict the survival period of LTx recipients.

Objective To establish a method for acquisition, perfusion, preservation and transportation of the genetically modified pig kidneys. Methods An eight genetically modified pig was utilized as experimental subject. Prior to kidneys procurement, the health status of the pig was assessed through hematology examination, and the vascular structure of the kidneys was examined using imaging techniques. Following kidneys acquisition, the pig kidneys were perfused and subsequently packaged into the cryogenic storage container labeled "For Organ Transportation Only" for interprovincial transport after communicating the transportation process with transportation department. To evaluate pathological damage to the pig kidneys, a serious of methods were employed such as hematoxylin-eosin (HE) staining, real-time fluorescent quantitative polymerase chain reaction (RT-qPCR), terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) fluorescence staining and enzyme-linked immune absorbent assay (ELISA). Results The preoperative examination of the eight genetically modified pig showed that the serum creatinine was 73.2 μmol/L, blood urea nitrogen was 2.8 mmol/L and hemoglobin was 116 g/L, all within the normal range, indicating normal renal function. CT angiography revealed no lesions in the pig kidneys, and no dilation, stenosis or premature branching of the blood vessels. The total time of obtaining the left and right kidneys from the eight genetically modified pig was (125 ± 10) min, with a blood loss of (20 ± 2) mL. The warm ischemia times were 3 min and 7 min, respectively. The perfusion and trimming times of the left and right kidneys were 36 min and 41 min, respectively. After perfusion, both kidneys were white and moist. The cold preservation and transportation time was 8 h. HE staining showed that some glomeruli were shrunk, and the lumens of the surrounding renal tubules were slightly depressed and swollen with partial inner membrane shedding and microvacuoles formed when the kidneys were preserved for 8 h. The level of cysteinyl aspartate-specific proteinase-3 messenger RNA in the kidneys tissue gradually increased with the extension of cold preservation time after 2 h (P<0.05). TUNEL fluorescence staining showed that only a small number of cells underwent apoptosis after 8 h of cold preservation, which was not significantly different from that at 0 h (P>0.05). ELISA results showed that the contents of lactate dehydrogenase (LDH) and creatinine in the preservation solution remained relatively stable, but the content of kidney injury molecule 1 (KIM-1) gradually increased with the extension of preservation time, suggesting that the pig kidneys had mild injury. Conclusions By establishing methods for acquisition, perfusion, preservation and transportation of the kidneys from genetically modified donor pig, it is possible to effectively and reliably use genetically modified pig kidneys for xenotransplantation.

[Objective] To establish a method for detecting the potency of recombinant human coagulation factor Ⅶa for injection. [Methods] By adding the sample and factor Ⅶ deficient plasma to the sample cup and activating the reaction with prothrombin time assay reagent (PT reagent), the coagulation time of the sample was determined by the change in magnetic bead swing amplitude in the sample cup. The logarithm of coagulation time was inversely proportional to the logarithm of human factor Ⅶa potency. [Results] Under the experimental conditions, the specificity of the methodology was evaluated through spiked recovery, and the recovery rates ranged from 90.0% to 110.0%. Within the range from 0.125 to 1.000 IU/mL, there was a good linear response between the potency and coagulation time of the standard and sample, with correlation coefficients r>0.99. As for the accuracy and repeatability, the recovery rates of various concentrations detected in the stock solution were 101.0%, 100.0% and 112.0%, respectively, with RSD values of 2.6%, 4.0% and 0.0%, respectively. The recovery rates of various concentrations in finished product testing were 104.0%, 94.7% and 112.0%, respectively, with RSD values of 1.9%, 2.4% and 0.0%, respectively. As for the intermediate precision, the RSD were 4.5% and 3.7%, respectively. After treated with sample diluent, the sample was tested at room temperature for 6 hours and still exhibited relatively stable biological activity. [Conclusion] This detection method is accurate, stable, easy to operate and highly automated, and is suitable for detecting the potency of recombinant human coagulation factor Ⅶa for Injection.

Lung transplantation is currently the only recognized effective treatment for end-stage lung disease and has improved the quality of life for patients. However, lung transplantation still faces many challenges, including rejection, infection, post-transplant acute kidney injury, post-transplant diabetes mellitus, ischemia-reperfusion injury and donor shortage, etc. Chinese lung transplantation scholars made a series of important progress in the field of clinical research in 2024, focusing on the study and solution of the above problems, and providing new ideas for lung transplantation surgery. This article systematically reviews the clinical research and technological innovation in the field of lung transplantation in 2024, summarizes the achievements of clinical research in the field of lung transplantation in China in 2024, and aims to providing new directions and strategies for future research.